CN113590295A - Task scheduling method and system based on quantum-behaved particle swarm optimization - Google Patents

Task scheduling method and system based on quantum-behaved particle swarm optimization Download PDF

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CN113590295A
CN113590295A CN202110873736.XA CN202110873736A CN113590295A CN 113590295 A CN113590295 A CN 113590295A CN 202110873736 A CN202110873736 A CN 202110873736A CN 113590295 A CN113590295 A CN 113590295A
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桑永宣
王博
楚杨阳
曹洁
崔霄
王昌海
黄万伟
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Zhengzhou University of Light Industry
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Abstract

The invention relates to a task scheduling method and a task scheduling system based on quantum behavior particle swarm optimization, wherein position data of each particle on each dimension is obtained, the position data of each particle is analyzed into a task scheduling scheme, the fitness value of each particle is evaluated, the local optimal fitness value of each particle and the global optimal fitness value corresponding to all the particles are updated, the corresponding particle positions are recorded, if iteration is not completed, the position of each particle is updated by using the motion mode of the quantum particle, then the particle position data analysis process is returned to be executed, and if iteration is completed, the particle position corresponding to the global optimal fitness value is analyzed into the task scheduling scheme. The task scheduling method based on quantum behavior particle swarm optimization provided by the invention can utilize the global search capability of the quantum behavior particle swarm optimization algorithm to realize reliable and effective task scheduling and obtain an efficient task scheduling scheme.

Description

Task scheduling method and system based on quantum-behaved particle swarm optimization
Technical Field
The invention relates to a task scheduling method and system based on quantum behavior particle swarm optimization.
Background
Through the development of ten years, the application of cloud computing is seen everywhere in real life, the low resource utilization efficiency of a cloud computing platform is one of the most concerned problems of cloud service providers, and task scheduling is one of effective methods for improving the resource utilization efficiency by scheduling each user request to a proper cloud server for processing. However, the heterogeneity of resources and the diversity of tasks make the design of an efficient task scheduling method difficult.
The current task scheduling method usually uses a heuristic method or a meta-heuristic method. Heuristic methods obtain an optimized task scheduling scheme by iteratively selecting a locally optimal solution, which is less time-consuming but generally of limited performance. The meta-heuristic method has the capability of obtaining the global optimal solution by combining a random method and local search, and can generally obtain the performance superior to that of the heuristic method, wherein the particle swarm optimization algorithm is a representative meta-heuristic method. However, the existing task scheduling method cannot reliably and effectively schedule the tasks.
Disclosure of Invention
In view of this, the present invention provides a task scheduling method and system based on quantum-behaved particle swarm optimization.
In order to solve the problems, the invention adopts the following technical scheme:
a task scheduling method based on quantum behavior particle swarm optimization comprises the following steps:
step 1: acquiring position data of each particle in each dimension;
step 2: analyzing the position data of each particle into a task scheduling scheme, and evaluating the fitness value of each particle;
and step 3: for each particle, updating the local optimal fitness value of each particle according to the current fitness value and the local optimal fitness value, recording the corresponding particle position, updating the global optimal fitness value corresponding to all the particles according to the current maximum fitness value and the global optimal fitness value in all the particles, and recording the corresponding particle position;
and 4, step 4: accumulating the current iteration times by 1, comparing the current iteration times with a preset maximum iteration time, updating the position of each particle by using the motion mode of the quantum particle if the current iteration times are less than the maximum iteration times, and then returning to execute the step 2; and if the current iteration times are equal to the maximum iteration times, resolving the particle position corresponding to the global optimal fitness value into a task scheduling scheme.
Further, in step 1, the position data of the jth particle is expressed as a T-dimensional vector<tj1，tj2，……，tjT>T is the number of tasks to be scheduled; the position range of each particle on each dimension is 1-P, wherein P is the number of cloud servers; setting the local optimal fitness value and the global optimal fitness value of each particle to be 0, and setting the current iteration times to be 0;
in step 2, analyzing the position data of each particle into a task scheduling scheme, and evaluating a fitness value of each particle, including:
the position t of the jth particle in the ith dimensionjiIndicating scheduling of the ith task to the sjiOn a cloud server, where sjiIs to satisfy tji–1＜sji≤tjiInteger of (2), position data of jth particle<tj1，tj2，……，tjT>The cloud servers correspond to the T tasks which are respectively scheduled; on each cloud server, a preset deadline first scheduling algorithm is used for determining the execution sequence of each task, the deadline is accumulated to obtain the number of the tasks meeting the deadline, and all cloudsThe accumulated result on the server is the fitness value of the particle.
Further, in step 3, for each particle, updating the local optimal fitness value of each particle according to the current fitness value and the local optimal fitness value includes:
if the current fitness value is larger than the local optimal fitness value, updating the local optimal fitness value into the current fitness value;
updating the global optimal fitness values corresponding to all the particles according to the current maximum fitness value and the global optimal fitness value in all the particles, wherein the updating comprises the following steps:
and if the current maximum fitness value is larger than the global optimal fitness value, updating the global optimal fitness value to the current maximum fitness value in all the particles.
Further, in the step 4, updating the position of each particle by using the motion mode of the quantum particle includes:
the following updating formula is adopted for updating:
Figure BDA0003189646750000031
wherein, tjiThe position of the jth particle in the ith dimension; u. ofjiAnd rjiAre all preset parameters and are at (0, 1);
Figure BDA0003189646750000032
pbjithe value of the position corresponding to the current local optimal fitness value of the jth particle in the ith dimension is obtained, and N is the number of the particles; a is a preset contraction and expansion factor; c. CjiFor the position of the center of the potential well where the jth particle is located in the ith dimension, the calculation formula is as follows:
cji＝yji·pbji+(1-yji)·gbi
wherein y isjiIs a preset parameter, at (0, 1); gbiAnd obtaining the value of the particle position corresponding to the current global optimal fitness value on the ith dimension.
A task scheduling system based on quantum behavior particle swarm optimization comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the task scheduling method based on quantum behavior particle swarm optimization when executing the computer program.
The invention has the beneficial effects that: firstly, acquiring position data of each particle on each dimension, analyzing the position data of each particle into a task scheduling scheme, evaluating the fitness value of each particle, updating the local optimal fitness value of each particle according to the current fitness value and the local optimal fitness value aiming at each particle, recording the corresponding particle position, updating the global optimal fitness value corresponding to all the particles according to the current maximum fitness value and the global optimal fitness value in all the particles, recording the corresponding particle position, performing repeated iteration on the process, updating the position of each particle by using the motion mode of the quantum particle when the iteration number is less than the maximum iteration number, analyzing the position data of each particle into the task scheduling scheme again, and evaluating the fitness value of each particle until the iteration number is equal to the maximum iteration number, and when the iteration times are equal to the maximum iteration times, analyzing the particle positions corresponding to the global optimal fitness value into a task scheduling scheme. Therefore, the task scheduling method based on quantum behavior particle swarm optimization provided by the invention can utilize the global search capability of the quantum behavior particle swarm optimization algorithm to realize reliable and effective task scheduling and obtain an efficient task scheduling scheme.
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In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings needed to be used in the embodiment will be briefly described as follows:
fig. 1 is a schematic overall flow chart of a task scheduling method based on quantum-behaved particle swarm optimization according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
In order to explain the technical means described in the present application, the following description will be given by way of specific embodiments.
Referring to fig. 1, it is a flowchart of an implementation process of a task scheduling method based on quantum-behaved particle swarm optimization provided in an embodiment of the present application, and for convenience of description, only a part related to the embodiment of the present application is shown.
Step 1: acquiring position data of each particle in each dimension:
the position data of each particle in each dimension is acquired, and the position data of each particle in each dimension can be randomly generated or acquired according to related rules. The position data of the jth particle is expressed as a T-dimensional vector<tj1，tj2，……，tjT>And T is the number of tasks to be scheduled. The position range of each particle in each dimension is 1-P, wherein P is the number of cloud servers. The number of particles is set according to actual needs, such as the number of tasks, and the number of particles is represented by N. Step 1 may initialize the local optimal fitness value and the global optimal fitness value of each particle and the current iteration number, that is, setting both the local optimal fitness value and the global optimal fitness value of each particle to 0, and setting the current iteration number to 0.
Step 2: the position data of each particle is analyzed into a task scheduling scheme, and the fitness value of each particle is evaluated:
analyzing the position data of each particle into a task scheduling scheme, and evaluating the fitness value of each particle, wherein a specific implementation process is given as follows:
the position t of the jth particle in the ith dimensionjiIndicating scheduling of the ith taskTo sjiOn a cloud server, where sjiIs to satisfy tji–1＜sji≤tjiAt this time, position data of the jth particle<tj1，tj2，……，tjT>Corresponding to the cloud servers to which the T tasks are respectively scheduled. And on each cloud server, determining the execution sequence of each task by using a preset deadline first scheduling algorithm, and accumulating the deadline to obtain the number of the tasks which are met, wherein the accumulated result on all the cloud servers is the fitness value of the particle. Because the deadline first scheduling algorithm is the existing algorithm, the detailed process is not described any more.
And step 3: for each particle, updating the local optimal fitness value of each particle according to the current fitness value and the local optimal fitness value, recording the corresponding particle position, updating the global optimal fitness value corresponding to all the particles according to the current maximum fitness value and the global optimal fitness value in all the particles, and recording the corresponding particle position:
wherein, aiming at each particle, according to the current fitness value and the local optimal fitness value, updating the local optimal fitness value of each particle, and recording the corresponding particle position, the method comprises the following steps: and for any particle, if the current fitness value is larger than the corresponding local optimal fitness value, updating the local optimal fitness value to the current fitness value, and recording the corresponding particle position.
According to the current maximum fitness value and the global optimal fitness value in all the particles, updating the global optimal fitness value corresponding to all the particles, and recording the corresponding particle positions, wherein the steps comprise: and if the current maximum fitness value in all the particles (namely the particle swarm) is larger than the global optimal fitness value, updating the global optimal fitness value into the current maximum fitness value in all the particles, and recording the corresponding positions of the particles.
And 4, step 4: accumulating the current iteration times by 1, comparing the current iteration times with a preset maximum iteration time, updating the position of each particle by using the motion mode of the quantum particle if the current iteration times are less than the maximum iteration times, and then returning to execute the step 2; if the current iteration times are equal to the maximum iteration times, analyzing the particle position corresponding to the global optimal fitness value as a task scheduling scheme:
and accumulating 1 for the current iteration times, and comparing the current iteration times with a preset maximum iteration time, wherein the maximum iteration time is set according to a cloud computing scene. If the current iteration number is smaller than the maximum iteration number, updating the position of each particle by using the motion mode of the quantum particle, and then returning to execute the step 2, wherein the updating the position of each particle by using the motion mode of the quantum particle comprises the following steps:
the following updating formula is adopted for updating:
Figure BDA0003189646750000071
wherein, tjiThe position of the jth particle in the ith dimension; u. ofjiAnd rjiAre all preset parameters and are (0, 1), ujiAnd rjiAre independent of each other, can be equal or different, i.e. ujiAnd rjiAll can be mutually independent random numbers between 0 and 1; ziThe mean value of the position corresponding to the current local optimum fitness value of all the particles in the ith dimension, i.e.
Figure BDA0003189646750000072
pbjiThe value of the position corresponding to the current local optimal fitness value of the jth particle in the ith dimension is obtained, and N is the number of the particles; a is a preset contraction and expansion factor which can be dynamically adjusted along with the iteration times according to an application scene; c. CjiFor the position of the center of the potential well where the jth particle is located in the ith dimension, the calculation formula is as follows:
cji＝yji·pbji+(1-yji)·gbi
wherein y isjiIs a preset parameter at (0, 1), i.e., yjiCan be a random number between 0 and 1; gbiAnd obtaining the value of the particle position corresponding to the current global optimal fitness value on the ith dimension.
If the current iteration times are equal to the maximum iteration times, the positions of the particles corresponding to the global optimal fitness value are analyzed into a task scheduling scheme, namely a solving result of the scheduling method, the analyzing process is shown in step 2, and the scheduling method is ended.
The task scheduling method provided by the invention improves the capability of searching the optimal scheduling scheme by using a quantum behavior particle swarm optimization method, thereby obtaining the most efficient and reliable scheduling scheme or the scheduling scheme close to the most efficient and reliable scheduling scheme.
The embodiment also provides a task scheduling system based on quantum behavior particle swarm optimization, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the task scheduling method based on quantum behavior particle swarm optimization when executing the computer program.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (5)

1. A task scheduling method based on quantum behavior particle swarm optimization is characterized by comprising the following steps:
step 1: acquiring position data of each particle in each dimension;
step 2: analyzing the position data of each particle into a task scheduling scheme, and evaluating the fitness value of each particle;
and step 3: for each particle, updating the local optimal fitness value of each particle according to the current fitness value and the local optimal fitness value, recording the corresponding particle position, updating the global optimal fitness value corresponding to all the particles according to the current maximum fitness value and the global optimal fitness value in all the particles, and recording the corresponding particle position;
and 4, step 4: accumulating the current iteration times by 1, comparing the current iteration times with a preset maximum iteration time, updating the position of each particle by using the motion mode of the quantum particle if the current iteration times are less than the maximum iteration times, and then returning to execute the step 2; and if the current iteration times are equal to the maximum iteration times, resolving the particle position corresponding to the global optimal fitness value into a task scheduling scheme.
2. The quantum-behaved particle-swarm-optimization-based task scheduling method according to claim 1,
in step 1, the position data of the jth particle is expressed as a T-dimensional vector<tj1，tj2，……，tjT>T is the number of tasks to be scheduled; the position range of each particle on each dimension is 1-P, wherein P is the number of cloud servers; setting the local optimal fitness value and the global optimal fitness value of each particle to be 0, and setting the current iteration times to be 0;
in step 2, analyzing the position data of each particle into a task scheduling scheme, and evaluating a fitness value of each particle, including:
the position t of the jth particle in the ith dimensionjiIndicating scheduling of the ith task to the sjiOn a cloud server, where sjiIs to satisfy tji–1＜sji≤tjiInteger of (2), position data of jth particle<tj1，tj2，……，tjT>The cloud servers correspond to the T tasks which are respectively scheduled; and on each cloud server, determining the execution sequence of each task by using a preset deadline first scheduling algorithm, and accumulating the deadline to obtain the number of the tasks which are met, wherein the accumulated result on all the cloud servers is the fitness value of the particle.
3. The quantum-behaved particle-swarm-optimization-based task scheduling method according to claim 1,
in step 3, for each particle, updating the local optimal fitness value of each particle according to the current fitness value and the local optimal fitness value, including:
if the current fitness value is larger than the local optimal fitness value, updating the local optimal fitness value into the current fitness value;
updating the global optimal fitness values corresponding to all the particles according to the current maximum fitness value and the global optimal fitness value in all the particles, wherein the updating comprises the following steps:
and if the current maximum fitness value is larger than the global optimal fitness value, updating the global optimal fitness value to the current maximum fitness value in all the particles.
4. The quantum-behaved particle-swarm-optimization-based task scheduling method according to claim 1,
in step 4, updating the position of each particle by using the motion mode of the quantum particle includes:
the following updating formula is adopted for updating:
Figure FDA0003189646740000021
wherein, tjiThe position of the jth particle in the ith dimension; u. ofjiAnd rjiAre all preset parameters and are at (0, 1);
Figure FDA0003189646740000022
pbjithe value of the position corresponding to the current local optimal fitness value of the jth particle in the ith dimension is obtained, and N is the number of the particles; a is a preset contraction and expansion factor; c. CjiFor the position of the center of the potential well where the jth particle is located in the ith dimension, the calculation formula is as follows:
cji＝yji·pbji+(1-yji)·gbi
wherein y isjiIs a preset parameter, at (0, 1); gbiIs at presentAnd the particle position corresponding to the global optimal fitness value is the value of the ith dimension.
5. A task scheduling system based on quantum-behaved particle-swarm optimization, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the quantum-behaved particle-swarm-optimization-based task scheduling method according to any one of claims 1 to 4.
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Application publication date: 20211102